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Intelligent Management of Measurement Units Equivalences in Food Databases

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Advances in Artificial Intelligence (CAEPIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

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Abstract

It is currently well-known that diet plays an important role in the promotion of healthy lifestyle and the prevention of chronic diseases. The Diet4You project is conceived to support the creation of an intelligent decision support system that provides personalized menus fitting a nutritional plan and taking into account the characteristics, needs and preferences of the person. The system involves a background food database, recording a collection of foods and prepared dishes with their standard portions as well as their nutritional decomposition in different food families. This DB is used to search the best combination of dishes approaching the total intake of different nutrients specified in the prescribed nutritional plan. The available background databases, specify the quantities of standard portions of several foods based on different measurement units which are not standardized, and it happens that the weight specified by one cup of melon is different from that of one cup of berries, among others. This arises the need of applying variable conversion factors to the dish description, before assessing whereas the total quantities of a certain menu fit well to the prescription. In this paper, a knowledge based approach is presented to the automatically management. An annotated reference food ontology is built on the basis of additional documentation. However the granularity of the information provided is heterogeneous and non exhaustive. The ontology-based missing values imputation is presented to overcome this limitations.

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Acknowledgements

Work supported by projects Diet4You (TIN2014-60557-R, Spanish Government) and IDEAI (SGR2017-574, Catalan Government).

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Authors and Affiliations

  1. Department of Statistics and Operations Research, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), Barcelona, Spain

    Beatriz Sevilla-Villanueva & Karina Gibert

  2. Department of Computer Science, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), Barcelona, Spain

    Miquel Sànchez-Marrè

  3. Knowledge Engineering and Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), Barcelona, Spain

    Beatriz Sevilla-Villanueva, Karina Gibert & Miquel Sànchez-Marrè

Authors
  1. Beatriz Sevilla-Villanueva
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  2. Karina Gibert
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  3. Miquel Sànchez-Marrè
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Corresponding author

Correspondence to Beatriz Sevilla-Villanueva .

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Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Sevilla-Villanueva, B., Gibert, K., Sànchez-Marrè, M. (2018). Intelligent Management of Measurement Units Equivalences in Food Databases. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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